3 research outputs found

    SmartAgro-Spectral: Teknik Pengukuran Kandungan Nitrit Pada Sarang Burung Walet Berbasis Spektral Menggunakan Metode Regresi Linier

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    Indonesia is the largest exporter of edible bird’s nest (EBN) to China, involving many EBN farmers from various regions in Indonesia. Therefore, a portable device is needed to rapidly and accurately measure the required quality of SBW to avoid rejection by Chinese buyers, which could result in significant losses. Consequently, for this purpose, an electronic instrument has been developed. smartAgro-Spectral is a microcontroller-based electronic instrument that measures nitrite content in edible bird’s nest (EBN) using linear regression method in machine learning calculations. This instrument can measure nitrite content based on the intensity of colors produced by EBN products. The coloring process is carried out by mixing EBW powder with Sulphanilamide solution and N-(1-naphthyl) Ethylenediamine Dihydrochloride (NED) solution. The concentration of EBN solution is normalized to values between 0.2 ppm and 0.7 ppm. The measurement process is carried out by emitting 18 waves of the light spectrum. The intensity of the 18 wavelengths of the measured light spectrum was selected based on the strong correlation between the intensity of the light spectrum and the value of nitrite content in the EBN product. The measurement results show that the intensity of the light spectrum that has a strong linear correlation is at wavelengths of 460 nm, 485 nm, 510 nm, 535 nm, and 610 nm. So, smartAgro-Spectral electronic instruments can be realized based on the intensity relationship of each wavelength through multiple linear regression analysis, and are able to linearly measure nitrite content in EBW products with a precision level of 99.85% and an accuracy rate of 99.85%

    Impurities Detection in Intensity Inhomogeneous Edible Bird’s Nest (EBN) Using a U-Net Deep Learning Model

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    As an important export, cleanliness control on edible bird’s nest (EBN) is paramount. Automatic impurities detection is in urgent need to replace manual practices. However, effective impurities detection algorithm is yet to be developed due to the unresolved inhomogeneous optical properties of EBN. The objective of this work is to develop a novel U-net based algorithm for accurate impurities detection. The algorithm leveraged the convolution mechanisms of U-net for precise and localized features extraction. Output probability tensors were then generated from the deconvolution layers for impurities detection and positioning. The U-net based algorithm outperformed previous image processing-based methods with a higher impurities detection rate of 96.69% and a lower misclassification rate of 10.08%. The applicability of the algorithm was further confirmed with a reasonably high dice coefficient of more than 0.8. In conclusion, the developed U-net based algorithm successfully mitigated intensity inhomogeneity in EBN and improved the impurities detection rate
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